| General | ||
|---|---|---|
| Filename(s) | sub-NDARTA920XFC_task-symbolSearch_run-1_eeg.set | |
| MNE object type | RawEEGLAB | |
| Measurement date | Unknown | |
| Participant | sub-NDARTA920XFC | |
| Experimenter | Unknown | |
| Acquisition | ||
| Duration | 00:02:35 (HH:MM:SS) | |
| Sampling frequency | 500.00 Hz | |
| Time points | 77,146 | |
| Channels | ||
| EEG | ||
| EOG | ||
| Head & sensor digitization | 131 points | |
| Filters | ||
| Highpass | 0.00 Hz | |
| Lowpass | 250.00 Hz | |
| General | ||
|---|---|---|
| Filename(s) | sub-NDARTA920XFC_task-symbolSearch_run-1_eeg.set | |
| MNE object type | RawEEGLAB | |
| Measurement date | Unknown | |
| Participant | sub-NDARTA920XFC | |
| Experimenter | Unknown | |
| Acquisition | ||
| Duration | 00:02:35 (HH:MM:SS) | |
| Sampling frequency | 500.00 Hz | |
| Time points | 77,146 | |
| Channels | ||
| EEG | ||
| EOG | ||
| Head & sensor digitization | 131 points | |
| Filters | ||
| Highpass | 1.00 Hz | |
| Lowpass | 100.00 Hz | |
| General | ||
|---|---|---|
| MNE object type | Epochs | |
| Measurement date | Unknown | |
| Participant | sub-NDARTA920XFC | |
| Experimenter | Unknown | |
| Acquisition | ||
| Total number of events | 24 | |
| Events counts | rest: 24 | |
| Time range | 0.000 – 0.500 s | |
| Baseline | off | |
| Sampling frequency | 500.00 Hz | |
| Time points | 251 | |
| Metadata | No metadata set | |
| Channels | ||
| EEG | ||
| Head & sensor digitization | 131 points | |
| Filters | ||
| Highpass | 1.00 Hz | |
| Lowpass | 100.00 Hz | |
No epochs exceeded the rejection thresholds. Nothing was dropped.
| Method | picard |
|---|---|
| Fit parameters | ortho=False extended=True max_iter=500 |
| Fit | 396 iterations on epochs (6024 samples) |
| ICA components | 127 |
| Available PCA components | 128 |
| Channel types | eeg |
| ICA components marked for exclusion | ICA000 ICA001 ICA002 ICA003 ICA004 ICA006 ICA007 ICA008 ICA009 ICA010 ICA011 ICA013 ICA014 ICA015 ICA019 ICA020 ICA024 ICA027 ICA032 ICA034 ICA039 ICA041 ICA043 ICA056 ICA063 ICA065 ICA070 ICA091 ICA109 |
| General | ||
|---|---|---|
| MNE object type | Epochs | |
| Measurement date | Unknown | |
| Participant | sub-NDARTA920XFC | |
| Experimenter | Unknown | |
| Acquisition | ||
| Total number of events | 24 | |
| Events counts | rest: 24 | |
| Time range | 0.000 – 0.500 s | |
| Baseline | off | |
| Sampling frequency | 500.00 Hz | |
| Time points | 251 | |
| Metadata | No metadata set | |
| Channels | ||
| EEG | ||
| EOG | ||
| misc | ||
| Head & sensor digitization | 131 points | |
| Filters | ||
| Highpass | 1.00 Hz | |
| Lowpass | 100.00 Hz | |
| Projections | Average EEG reference (off) | |
No epochs exceeded the rejection thresholds. Nothing was dropped.
| Method | picard |
|---|---|
| Fit parameters | ortho=False extended=True max_iter=500 |
| Fit | 396 iterations on epochs (6024 samples) |
| ICA components | 127 |
| Available PCA components | 128 |
| Channel types | eeg |
| ICA components marked for exclusion | ICA000 ICA001 ICA002 ICA003 ICA004 ICA006 ICA007 ICA008 ICA009 ICA010 ICA011 ICA013 ICA014 ICA015 ICA019 ICA020 ICA024 ICA027 ICA032 ICA034 ICA039 ICA041 ICA043 ICA056 ICA063 ICA065 ICA070 ICA091 ICA109 |
| General | ||
|---|---|---|
| Filename(s) | sub-NDARTA920XFC_task-symbolSearch_run-1_proc-eyelink_raw.fif | |
| MNE object type | Raw | |
| Measurement date | Unknown | |
| Participant | sub-NDARTA920XFC | |
| Experimenter | Unknown | |
| Acquisition | ||
| Duration | 00:02:08 (HH:MM:SS) | |
| Sampling frequency | 500.00 Hz | |
| Time points | 63,996 | |
| Channels | ||
| EEG | ||
| EOG | ||
| misc | ||
| Head & sensor digitization | 131 points | |
| Filters | ||
| Highpass | 1.00 Hz | |
| Lowpass | 100.00 Hz | |
| General | ||
|---|---|---|
| Filename(s) | sub-NDARTA920XFC_task-symbolSearch_proc-ica_epo.fif | |
| MNE object type | EpochsFIF | |
| Measurement date | Unknown | |
| Participant | sub-NDARTA920XFC | |
| Experimenter | Unknown | |
| Acquisition | ||
| Total number of events | 24 | |
| Events counts | rest: 24 | |
| Time range | 0.000 – 0.500 s | |
| Baseline | off | |
| Sampling frequency | 500.00 Hz | |
| Time points | 251 | |
| Metadata | No metadata set | |
| Channels | ||
| EEG | ||
| EOG | ||
| misc | ||
| Head & sensor digitization | 131 points | |
| Filters | ||
| Highpass | 1.00 Hz | |
| Lowpass | 100.00 Hz | |
| Projections | Average EEG reference (on) | |
No epochs exceeded the rejection thresholds. Nothing was dropped.
import mne
bids_root = "mergedDataset"
deriv_root = "mergedDataset/derivatives"
subjects_dir = None
#subjects = ["NDARAB678VYW","NDARDC504KWE","NDARDL033XRG","NDARTK720LER","NDARDZ794ZVP"] #"all" #["NDARAG429CGW"]
#subjects = ["NDARDZ794ZVP"] #"all" #["NDARAG429CGW"]
#subjects = ["NDARAB678VYW"] ##["NDARKM301DY0"] #"all"
#subjects = ["NDARAF535XK6"]
#subjects = ["NDARUC804LKP","NDARVD609JNZ","NDARGN483WFH","NDARFY623ZTE","NDARFN221WW5","NDARXR865BVX","NDARRK528GFZ","NDARKP823HEM","NDARYA857NDW","NDARUR298LVX","NDARWT403LP6","NDARDL033XRG","NDARYJ413BLN","NDARLP413TUX","NDARLM981MEN","NDARKG016KD1","NDARAG788YV9","NDARUA035YJN","NDARNP381RZ4","NDARZG044CJ5","NDARHT518WEM","NDARDX544FJ0","NDARKM199DXW","NDARWC905XUG","NDARYH501UH3","NDARRK146XCZ"]
subjects = ["NDARKT714TXR","NDARUC804LKP","NDARGN483WFH","NDARAL897CYV","NDARTA920XFC","NDARPW746FWF","NDARDL033XRG","NDARRK528GFZ","NDARBT436PMT","NDARZK745JGG"]
ch_types = ["eeg"]
interactive = False
sessions = []#"all"
task = "symbolSearch"
#task_et = "WISC_ProcSpeed"
task_is_rest = True
rest_epochs_duration = 5
rest_epochs_overlap = 0
runs = ["1"]
et_has_run = False
et_has_task = True
epochs_tmin = 0
#rest_epochs_duration = 10
#rest_epochs_overlap = 0
baseline = None
#baseline: tuple[float | None, float | None] | None = (-0.2, 0)
#raw_resample_sfreq: float | None = 250
eeg_reference = "average"
ica_l_freq = 1 # ?
# determined by icalabel
l_freq: float | None = 1
h_freq: float | None = 100
ica_h_freq: float | None = 100
# data was recorded in the US
notch_freq = 60
on_error = "continue"
######### Remove these when doing Unfold analysis! ############
# positive / negative feedback
#conditions = ["HAPPY", "SAD"]
##conditions = ["Fixation L"]
##
##epochs_tmin: float = -0.5
##epochs_tmax: float = 2.6 # since feedback is so infrequent, long epochs are okay
##
##baseline: tuple[float | None, float | None] | None = (-0.2, 0)
###############################################################
spatial_filter = "ica"
# ica_n_components = 96 ?
ica_algorithm = "picard-extended_infomax"
#ica_use_ecg_detection: bool = True
#ica_use_eog_detection: bool = True
ica_use_icalabel = True
#ica_reject: dict[str, float] | Literal["autoreject_local"] | None = "autoreject_local"
ica_reject = "autoreject_local" #TESTING
reject = "autoreject_local" #TESTING
#These are identical, just ensuring compatibility
sync_eyelink = True
sync_eye = True
#sync_eventtype_regex = "\\d-trigger=10 Image moves"
#sync_eventtype_regex_et = "trigger=10 Image moves"
#Contrast detection
#sync_eventtype_regex = r"contrastTrial_start"
#sync_eventtype_regex_et = r"# Message: 15"
sync_eventtype_regex = r"(?:trialResponse|newPage)" #r"trialResponse"
sync_eventtype_regex_et = r"# Message: (?:14|20)" #r"# Message: 14"
#sync_eventtype_regex = r"trialResponse"
#sync_eventtype_regex_et = r"# Message: 14"
#eog_channels = ["HEOGL", "HEOGR", "VEOGL", "VEOGU"]
#eeg_bipolar_channels = {"HEOG": ("HEOGL", "HEOGR"), "VEOG": ("VEOGL", "VEOGU")}
#eog_channels = ["HEOG", "VEOG"]
#sync_heog_ch = ("HEOG")
#eeg_bipolar_channels = {"HEOG": ("E40", "E109"),
# "VEOG": ("E21", "E127")} #left eye
##eeg_bipolar_channels = {
## #"HEOG": ("E127", "E126"),
## "HEOG": ("E126", "E127"),
## "VEOG": ("E22", "E127"), #left eye
##}
eeg_bipolar_channels = {
#"HEOG": ("E127", "E126"),
# Version 1, doesn't work well
#### "HEOG": ("E127", "E126"),
#### "VEOG": ("E22", "E127"), #left eye
# Version 2, works well but not sure why
###"HEOG": ("E109", "E40"),
###"VEOG": ("E22", "E127"),
# Version 3, seems to work well?
"HEOG": ("E2", "E26"),
"VEOG": ("E3", "E8")
}
eog_channels = ["HEOG", "VEOG"]
sync_heog_ch = "HEOG"
sync_et_ch = ("L POR X [px]", "R POR X [px]")
#sync_et_ch = "xpos_right"
sync_plot_samps = 3000
decode: bool = False
run_source_estimation = False
montage = mne.channels.make_standard_montage("GSN-HydroCel-128")
eeg_template_montage = montage
drop_channels = ["Cz"]
n_jobs = 1
Platform Linux-5.14.0-427.96.1.el9_4.x86_64-x86_64-with-glibc2.34
Python 3.11.7 (main, Aug 29 2025, 00:00:00) [GCC 11.4.1 20231218 (Red Hat 11.4.1-4)]
Executable /pfs/work9/workspace/scratch/st_st156392-mydata/mnevenv/bin/python
CPU Intel(R) Xeon(R) Platinum 8358 CPU @ 2.60GHz (128 cores)
Memory 251.5 GiB
Core
├☑ mne 1.10.2 (latest release)
├☑ numpy 2.3.4 (OpenBLAS 0.3.30 with 1 thread)
├☑ scipy 1.16.2
└☑ matplotlib 3.10.7 (backend=agg)
Numerical (optional)
├☑ sklearn 1.7.2
├☑ nibabel 5.3.2
├☑ pandas 2.3.3
├☑ h5io 0.2.5
├☑ h5py 3.15.1
└☐ unavailable numba, nilearn, dipy, openmeeg, cupy
Visualization (optional)
├☑ pyvista 0.46.3 (OpenGL 4.5 (Compatibility Profile) Mesa 23.3.3 via llvmpipe (LLVM 17.0.6, 256 bits))
├☑ pyvistaqt 0.11.3
├☑ vtk 9.5.2
└☐ unavailable qtpy, ipympl, pyqtgraph, mne-qt-browser, ipywidgets, trame_client, trame_server, trame_vtk, trame_vuetify
Ecosystem (optional)
├☑ mne-bids 0.17.0
├☑ mne-icalabel 0.8.1
├☑ mne-bids-pipeline 0.1.0.dev917+g2366e2b9a
├☑ eeglabio 0.1.2
├☑ edfio 0.4.10
├☑ pybv 0.7.6
└☐ unavailable mne-nirs, mne-features, mne-connectivity, neo, mffpy